:: Volume 5, Issue 2 (Summer 2018) ::
2018, 5(2): 293-303 Back to browse issues page
A Detection of Type2 Diabetes using C4.5 Decision Tree
Hamed Sabbagh Gol
M.Sc in Computer Engineering, Faculty of Computer, Department of Computer Engineering, Payame Noor University (PNU), Iran
Abstract:   (1113 Views)
Introduction: One of the most common diseases in the world is diabetes and the global prevalence of diabetes increases by about six percent annually. The use of data mining techniques to create predictive models is very helpful in identifying people at risk and reducing the complications of the disease. In this study, through using decision tree C4.5, methods of prevention and treatment of diabetes were investigated.
Methods: In this applied and descriptive study, we used the standard UCI data and the pima-Indians-diabetes data set. This database contains 768 records with 8 fields. The analysis was done using Weka software using the CRISP3 methodology. In modeling decision tree, C4.5 was created using input variables and determining target variables. Also, the sensitivity, specificity, accuracy, as well as positive and negative predictive values were used to evaluate the model.
Results: According to the model, high blood sugar levels, high gravidity, high age, high diastolic blood pressure, familial history and high BMI have respectively the highest effects on type 2 diabetes mellitus. The ranking rate was 73.8% and the accuracy of the C4.5 algorithm was 79%.
Conclusion: Compared to the results of studies in the field of data mining for diabetes, the accuracy of the proposed algorithm is acceptable. The most effective factors on diabetes were identified. Also, rules were developed that can be used as a model to predict the risk of diabetes in people.
Keywords: Data mining, Type2 diabetes, C4.5 Decision tree
Full-Text [PDF 966 kb]   (335 Downloads)    
Type of Study: Original Article | Subject: Data Mining
Received: 2018/02/14 | Accepted: 2018/07/12

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Volume 5, Issue 2 (Summer 2018) Back to browse issues page